Often the bottleneck in document classifica- tion is finding good representations that zoom in on the most important aspects of the doc- uments. Most research uses n-gram repre- sentations, but relevant features often occur discontinuously, e.g., not. . . good in sentiment analysis. In this paper we present experi- ments getting experts to provide regular ex- pressions, as well as crowdsourced annota- tion tasks from which regular expressions can be derived. Somewhat surprisingly, it turns out that these crowdsourced feature combina- tions outperform automatic feature combina- tion methods, as well as expert features, by a very large margin and reduce error by 24-41% over n-gram representations.